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稳健方差分析(Welch & Trimmed Mean)×Bootstrap Inference×Theil-Sen 估计器×
领域统计学统计学统计学
方法族Regression modelRegression modelRegression model
起源年份195119791968
提出者Welch (1951); robust trimmed-mean approach popularised by WilcoxBradley EfronHenri Theil (1950); P. K. Sen (1968)
类型Robust one-way analysis of varianceResampling-based inferenceRobust linear regression
开创性文献Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336. DOI ↗Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389. DOI ↗
别名Welch ANOVA, trimmed-mean ANOVA, heteroscedastic one-way ANOVA, Robust ANOVA (Welch & Trimmed Mean)bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap ÇıkarımıTheil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator
相关556
摘要Robust ANOVA compares the central tendency of three or more groups when the classical assumptions of normality and equal variances fail. It combines Welch's heteroscedasticity-adjusted statistic, introduced by Welch in 1951, with trimmed-mean tests advanced by Wilcox, giving reliable comparisons in the presence of outliers and unequal group spreads.Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%.
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ScholarGate方法对比: Robust ANOVA · Bootstrap Inference · Theil-Sen Estimator. 于 2026-06-18 检索自 https://scholargate.app/zh/compare